Gait recognition is a recognition technology based on biometrics, and is intended to identify a person according to the walking posture of the person. Gait is the walking posture of a person, comprising a regular movement trend and variations present at joints of the upper limbs and lower limbs during walking. By analyzing gait, identity and attribute, for example the information such as gender, age, race and the like, of a person to whom the gait belongs can be obtained. For the field of recognition technology, gait is biometrics of great potential, which mainly manifests in the following three aspects: 1) remotely accessible: surveillant can obtain gait information of a specific subject from a distance, and collect it secretly in a contactless manner, while the biometrics such as iris, fingerprint are collected with the need of a person's cooperation, which is very important in intelligent video surveillance; 2) robustness: even in low resolution videos, a gait feature still works well, and in contrast, an accurate face recognition and vocal print recognition impose relatively high requirements on the quality of data resources; 3) security: it is difficult to imitate or camouflage human gait, and if a person changes his/her gait in public deliberately, he/she would become more suspicious and gain attention. Gait recognition technology has become an important research direction in the computer vision and pattern recognition.
However, accurate gait recognition technology is not yet mature at present and there is still a huge challenge on how to perform accurate identity recognition by gait analysis. In the existing research field, gait recognition technology can be generally divided into two kinds: a model-based method and an appearance-based method. The model-based method performs matching by extracting human body structural features from a gait sequence, which imposes higher requirements on resolution of the collected images and is accompanied by complicated computation at the same time. The appearance-based method is suitable for gait recognition in outdoor applications and imposes fewer requirements on the resolution of the collected images. However, it is a big challenge on how to select discriminative feature. Nonetheless, it is difficult to extract an accurate gait feature from traditional hand-crafted gait features and to break the bottleneck of existing gait recognition technology.
Since gait recognition may be effected by factors such as viewpoints, clothing, belongings, walking speed, it causes too small inter-class differences between different persons and too large intra-class differences of a same person in different scenes for the gait feature for judging and recognizing in the prior art, and thus results in inaccurate recognition in the end.